ChatGPT and Claude Integration with Your Business Systems in 2026 (AI apps from €6,000 net)
Integrating ChatGPT or Claude with your business systems is three different purchases: a ChatGPT Team subscription, a thin API integration, or a full AI app with ERP/CRM integration. For most teams the right first purchase is the subscription. A dedicated app with integration starts from €6,000 net. You start with a free process scan.
Integrating ChatGPT or Claude with your business systems is not one purchase but three: a subscription (ChatGPT Team, Claude for Work), a thin API integration, or a full AI app with ERP or CRM integration. For most teams the right first step is the subscription. A dedicated app with integration starts from €6,000 net, and a thin automation from €3,500 net.
Quick answer
Before you count costs, settle which of the three things you are actually buying. These are three different budgets and three different risks, net:
- subscription (ChatGPT Team, Claude for Work): licences for people who are meant to talk to the model to work faster. A fraction of the cost of an implementation, minutes to start. For most teams, this is the right first purchase.
- thin API integration (from €3,500 net): one process wired to the model through an API, so that something happens automatically in your system, without a person copying data across.
- full AI app with integration (from €6,000 net): a project implementation with an interface, ERP or CRM integrations, search over your documents (RAG), boundaries, and a trail. Typical full implementations run €6,000–35,000 net.
The first step is free: a process scan (€0) is a 30-minute engineer call plus a written takeaway in two business days. If you want a portable document with architecture and a fixed quote before a bigger decision, the current price of the implementation specification is €1,200 net. The full price list is on the Syntalith pricing page, and the scope of the apps line on the custom AI apps page.
What does "integrating ChatGPT with a business system" actually mean?
Integration is connecting the model through an API to your logic and your data, so that the data and decisions stay under your control. It is not the same as buying a licence. When you buy ChatGPT Team, your people talk to the model in the vendor's chat window: they paste data in, copy answers out, and the business system knows nothing about it. When you integrate through an API, the model becomes part of your process: it reads a ticket from your inbox, pulls a record from CRM, drafts a reply by your rules, and writes the result back where the work really lives.
The difference comes down to three things: who does the work (with a subscription a person, with an integration the system, within the boundaries you set), where the data sits (in the vendor's chat or in your environment), and whether a trail remains that you can check after the fact.
"OpenAI API" and "Claude API" are the technical name for that connection. The choice of provider (OpenAI, Anthropic) is the smallest part of the decision. Models are interchangeable and getting cheaper; what is expensive and durable is everything else: the integrations, the data quality, and the boundaries.
When is a subscription (ChatGPT Team, Claude for Work) enough?
For most teams a subscription is enough, and that is the honest answer to start from. If you want people to write faster, summarise documents, draft copy, and tidy up notes, ChatGPT Team or Claude for Work gives them that immediately, at a fraction of the cost of any implementation. The work still belongs to the human, and that is fine, as long as that was the plan.
A subscription is the right first purchase when the goal is to speed people up, not run an automation; when the data they paste can be processed in the vendor's environment under its business plan; and when nothing has to happen by itself in your systems. Across the country this is still the norm: Poland's statistics office (GUS, via PIE, December 2025) reports that 8.7% of Polish companies used AI in 2025, and among small firms only 6.1%. Most teams are at the "give people a tool" stage, not "build our own system", and that is the cheaper way to learn.
If, after months on a subscription, you see people still manually moving the model's output into CRM, an ERP, or email, that is the signal you are ready for integration. But not the other way around: do not build an integration just to find out whether AI is useful at all.
When does a thin API integration win?
A thin API integration wins when the model has to DO something in your system automatically, not just answer a person. It is one process wired to the model's API and to one or two of your systems: the inbox reads tickets, the model classifies and drafts a reply, the system writes the result into the helpdesk. The human stops being the connector between a chat window and the business system.
This path makes sense when the process is repeatable and well described, crosses at most two systems, and lets you name the rules and the exceptions. It starts from €3,500 net, the scope of automating one process, not a full app. How to work out whether such an automation pays back, we lay out in the guide to automation cost and ROI.
The line is simple: if the process starts branching into many paths, crossing ERP, CRM, an inbox, and spreadsheets at once, or requiring reasoning over scattered documents, a thin integration runs out. Then the right purchase is a full app.
When is a full AI app (RAG, ERP/CRM integration) justified?
You build a full app when the process lives across several systems at once, needs to reach into your documents, or the data cannot leave your environment. This is a project implementation: an input interface or channel, integration with the model through an API, connections to ERP and CRM, a search layer over company documents (RAG), rules with boundaries and escalation, and data handling that meets GDPR. It starts from €6,000 net, and typical full implementations fall in the €6,000–35,000 net range.
Three signals say the moment has come: the process crosses several systems and today a person copies data between them; the tool has to write or execute something in your systems, not just suggest; or the data and logic are so sensitive or specific that no off-the-shelf tool handles them without workarounds. When a ready tool stops being enough and what exactly goes into such an MVP, we break down in the piece on custom AI apps. If the main goal is for employees to ask about internal procedures and get an answer with a cited source, that is a narrower case: an AI knowledge assistant built on RAG.
Subscription, thin integration, or full app: which path do you pick?
This is not a price list for the whole market, just a way to read the decision. The three paths are three different purchases, not three points on one price scale. The last two columns matter most: they, not the model name, decide what is right for you.
| Path | What you buy | Cost (net) | When it is enough | When it is the wrong choice |
|---|---|---|---|---|
| Subscription (ChatGPT Team, Claude for Work) | licences for people, model in a chat window | monthly fee per user | you want to speed people up, data can sit with the vendor, nothing has to happen by itself | you need action in your systems, or data cannot leave the company |
| Thin API integration | one process wired to the model and 1–2 systems | from €3,500 | a repeatable process, the model has to do something automatically, the rules can be named | the process lives across many systems, needs reasoning over scattered data |
| Full AI app with integration | an implementation with ERP/CRM, RAG, boundaries, and a trail | from €6,000 (full €6,000–35,000) | many systems, data under your control, the app does the work within boundaries | a typical process well served by an off-the-shelf tool; low, irregular volume |
What really drives the cost of integration?
The price of an integration is set not by the model but by four things around it. The choice between OpenAI and Claude is the cheapest and most easily reversible decision in the whole project.
Number and quality of integrations. Connecting one modern API is different work from wiring into an undocumented legacy system, an inbox, and Excel files. ERP or CRM integration is easy where the system has a clean API, and expensive where you have to work around its absence, stand up a middleware, or work off exports. This is the most common source of the price gap between two seemingly similar projects.
Data quality. Integrating a model with a mess produces a mess delivered in a confident tone. If the CRM data is inconsistent and the procedures on the drive are out of date, the first cost is tidying them up, not the model. That is work on the company's side and we name it plainly before we set a price. The EY Poland report (April 2026, 497 medium and large firms) finds that only 9% of surveyed companies have complete data infrastructure, and about half report disappointment or incomplete ROI from AI. The direction is clear: with poor data, an integration disappoints regardless of the model.
GDPR and the DPA. Personal data, financial information, a data processing agreement (DPA) with the provider, environment separation, and the decision about what leaves the company and for how long are not decorations. They are the conditions for going into production and a real part of the cost. On top of that comes the AI-literacy duty (Article 4 of the EU AI Act, in force since February 2025): staff and customers should know they are dealing with an AI tool.
Token cost, charged per query. Providers (OpenAI, Anthropic) bill usage by the number of input and output tokens. That means the cost is variable and charged for every query, not a fixed line in a subscription. At a typical company's volume it is usually a few cents per case, but it grows with volume and with context length. Plan this cost up front: set a daily limit, define behaviour when it is exceeded, and use a cheaper model for routine work and a pricier one only for hard cases. What that operating cost looks like after launch, we break down in the piece on what a running AI agent actually costs.
Boundaries and a trail: the difference between a demo and production
A demo integration can be built in an evening. Production is mostly what the demo does not show: boundaries and a trail. That difference separates an integration you can point at invoices and customers from a toy on a slide.
Boundaries. A production integration has a clear rule for what it may do without a human and what goes for approval. A model that sends replies to customers or changes data in an ERP by itself is a different risk from one that drafts something for approval. You set the boundaries, not the model provider.
A trail. If you cannot reconstruct after the fact what the system did and on what basis, it is not a production integration, it is roulette with a nice interface. A trail of every decision lets you check behaviour, catch an error, and answer an audit. It is also more expensive than the model itself, which is why it disappears from cheap offers.
There is one more risk few mention at the start: the documents and messages the model reads can contain hidden instructions that try to hijack its behaviour (prompt injection). In an integration reaching into many sources, this vector is handled at the architecture level, which we cover in the piece on prompt injection in AI agents.
When NOT to build an integration
Honestly: an integration is often a bad purchase, however fashionable it is. We will say so plainly at the scan, before you spend anything.
- The subscription is not yet exhausted. If you have not tested how much ChatGPT Team or Claude for Work already handles, an integration is premature. Squeeze the cheaper path first.
- Low, irregular volume. If the process happens rarely, the cost of building and maintaining it will not pay back even in an optimistic scenario. Manual handling can be cheaper.
- An unstable process or poor data. If the rules change every week and live in someone's head, and the data is scattered, order the process and the sources first. An integration launched on a mess entrenches the mess.
- The problem is missed calls or a simple customer chat. That is a different purchase: missed calls are for a voicebot (odbierze.ai), and repeat shop questions for sprzeda.ai, not a dedicated integration.
FAQ
What does it mean to integrate ChatGPT or Claude with a business system?
It means connecting the model through an API to your logic and your data, so that the data and decisions stay under your control, not in the vendor's chat window. Unlike a ChatGPT Team licence, something happens automatically in your systems. API integration starts at automation level (from €3,500 net), and a full app with ERP or CRM from €6,000 net.
When is ChatGPT Team or Claude for Work enough, and when do you need API integration?
For most teams the right first purchase is the subscription: it is enough when the goal is to speed people up. API integration wins only once the model has to do something in your systems automatically, when the process lives across several systems, or when data cannot go into the vendor's interface.
How much does integrating the OpenAI or Claude API with an ERP or CRM cost?
A thin integration of one process starts from €3,500 net, and a dedicated app with ERP or CRM integration from €6,000 net (full implementations €6,000–35,000). The price depends mainly on the number and quality of integrations and on data quality, not on the model itself.
How much do tokens cost when integrating the ChatGPT or Claude API?
Providers bill usage by input and output tokens, so the cost is variable and charged per query. At a typical company's volume it is usually a few cents per case, but it grows with volume and context length. Plan it up front: a daily limit, and a cheaper model for routine work and a pricier one for hard cases.
How to start
The cheapest sensible first step is to calculate the process and choose a path, not to order an integration straight away.
- Book a free process scan and show one process where people today manually move the model's output into a system.
- Prepare: whether you already use an AI subscription, which systems are in the path (ERP, CRM, inbox), how many cases a month, what the model would do by itself, and your data requirements (GDPR, DPA).
- After the call you get a recommendation: stay on the subscription, a thin API integration, a full app with integration, an implementation specification, or an honest "a licence is enough for now."
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